Besides individual neuron dynamics, network connections are also shown to play a key role in neuronal synchronization. To simulate the neuronal dynamic behavior in complex brain network, different network models have been proposed, including small-world network [1]}, [2]}, [3]}, Erdös-Rényi network [4]}, scale-free network [5]}, [6]}. In the Erdös-Rényi network, it was suggested that large connection probability promotes bursting synchronization [7]}. In small-world networks, adding some random connections instead of increasing rewiring probability, would increase neuronal synchronization [8]}. The state of synchronization can be various including complete synchrony, delay synchrony and phase locking [9]}. Considering the dynamical heterogeneity, one may have different synchronization properties, even for networks with the same propensity to synchronization [10]}. In pulse-coupled networks of bursting Hindmarsh-Rose neurons, the stability of completely synchronous state in such networks was found to solely depend on the number of signals each neuron received [11]}.
